{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Sample Submission.csv\n",
      "/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Test.csv\n",
      "/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Train.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "k9P0XCLW_8dQ",
    "outputId": "ec576901-cc36-4313-f31b-b7c4fa07d8be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting autoviml\n",
      "  Downloading autoviml-0.1.662-py3-none-any.whl (111 kB)\n",
      "\u001b[K     |████████████████████████████████| 111 kB 2.8 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: regex in /opt/conda/lib/python3.7/site-packages (from autoviml) (2020.4.4)\n",
      "Requirement already satisfied, skipping upgrade: jupyter in /opt/conda/lib/python3.7/site-packages (from autoviml) (1.0.0)\n",
      "Requirement already satisfied, skipping upgrade: seaborn in /opt/conda/lib/python3.7/site-packages (from autoviml) (0.10.0)\n",
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      "Requirement already satisfied, skipping upgrade: ipython in /opt/conda/lib/python3.7/site-packages (from autoviml) (7.13.0)\n",
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      "Requirement already satisfied, skipping upgrade: scikit-learn>=0.23.1 in /opt/conda/lib/python3.7/site-packages (from autoviml) (0.23.2)\n",
      "Requirement already satisfied, skipping upgrade: imbalanced-learn>=0.7 in /opt/conda/lib/python3.7/site-packages (from autoviml) (0.7.0)\n",
      "Requirement already satisfied, skipping upgrade: featuretools in /opt/conda/lib/python3.7/site-packages (from autoviml) (0.18.1)\n",
      "Collecting vaderSentiment\n",
      "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
      "\u001b[K     |████████████████████████████████| 125 kB 9.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: beautifulsoup4 in /opt/conda/lib/python3.7/site-packages (from autoviml) (4.9.0)\n",
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      "Requirement already satisfied, skipping upgrade: nbconvert in /opt/conda/lib/python3.7/site-packages (from jupyter->autoviml) (5.6.1)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied, skipping upgrade: ptyprocess>=0.5 in /opt/conda/lib/python3.7/site-packages (from pexpect; sys_platform != \"win32\"->ipython->autoviml) (0.6.0)\n",
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      "Requirement already satisfied, skipping upgrade: webencodings in /opt/conda/lib/python3.7/site-packages (from bleach->nbconvert->jupyter->autoviml) (0.5.1)\n",
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      "Requirement already satisfied, skipping upgrade: heapdict in /opt/conda/lib/python3.7/site-packages (from zict>=0.1.3->distributed>=2.12.0->featuretools->autoviml) (1.0.1)\n",
      "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /opt/conda/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter->autoviml) (1.6.0)\n",
      "Requirement already satisfied, skipping upgrade: attrs>=17.4.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter->autoviml) (19.3.0)\n",
      "Requirement already satisfied, skipping upgrade: pyrsistent>=0.14.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter->autoviml) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter->autoviml) (3.1.0)\n",
      "Installing collected packages: vaderSentiment, autoviml\n",
      "Successfully installed autoviml-0.1.662 vaderSentiment-3.3.2\n"
     ]
    }
   ],
   "source": [
    "!pip install autoviml --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "YI1spBqIBFWM",
    "outputId": "95058920-ad1c-4476-80bb-f4f5420deed5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading collection 'popular'\n",
      "[nltk_data]    | \n",
      "[nltk_data]    | Downloading package cmudict to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package cmudict is already up-to-date!\n",
      "[nltk_data]    | Downloading package gazetteers to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
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      "[nltk_data]    |     /usr/share/nltk_data...\n",
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      "[nltk_data]    | Downloading package inaugural to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package inaugural is already up-to-date!\n",
      "[nltk_data]    | Downloading package movie_reviews to\n",
      "[nltk_data]    |     /usr/share/nltk_data...\n",
      "[nltk_data]    |   Package movie_reviews is already up-to-date!\n",
      "[nltk_data]    | Downloading package names to /usr/share/nltk_data...\n",
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      "[nltk_data]    |       to-date!\n",
      "[nltk_data]    | \n",
      "[nltk_data]  Done downloading collection popular\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Imported Auto_NLP version: 0.0.42.. Call using:\n",
      "     train_nlp, test_nlp, nlp_pipeline, predictions = Auto_NLP(\n",
      "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
      "                modeltype='Classification',top_num_features=200, verbose=0,\n",
      "                build_model=True)\n",
      "Imported Auto_ViML version: 0.1.662. Call using:\n",
      "             m, feats, trainm, testm = Auto_ViML(train, target, test,\n",
      "                            sample_submission='',\n",
      "                            scoring_parameter='', KMeans_Featurizer=False,\n",
      "                            hyper_param='RS',feature_reduction=True,\n",
      "                             Boosting_Flag='CatBoost', Binning_Flag=False,\n",
      "                            Add_Poly=0, Stacking_Flag=False,Imbalanced_Flag=False,\n",
      "                            verbose=1)\n",
      "            \n",
      "To remove previous versions, perform 'pip uninstall autoviml'\n",
      "NEW! Now Auto_ViML comes with a feature_engineering module using featuretools library!\n",
      "To get the latest version, perform \"pip install autoviml --no-cache-dir --ignore-installed\"\n"
     ]
    }
   ],
   "source": [
    "from autoviml.Auto_ViML import Auto_ViML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Train.csv\")\n",
    "test = pd.read_csv(\"/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Test.csv\")\n",
    "samp = pd.read_csv(\"/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Sample Submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text_ID</th>\n",
       "      <th>Product_Description</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3057</td>\n",
       "      <td>The Web DesignerÛªs Guide to iOS (and Android...</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6254</td>\n",
       "      <td>RT @mention Line for iPad 2 is longer today th...</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8212</td>\n",
       "      <td>Crazy that Apple is opening a temporary store ...</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4422</td>\n",
       "      <td>The lesson from Google One Pass: In this digit...</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5526</td>\n",
       "      <td>RT @mention At the panel: &amp;quot;Your mom has a...</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Text_ID                                Product_Description  Product_Type  \\\n",
       "0     3057  The Web DesignerÛªs Guide to iOS (and Android...             9   \n",
       "1     6254  RT @mention Line for iPad 2 is longer today th...             9   \n",
       "2     8212  Crazy that Apple is opening a temporary store ...             9   \n",
       "3     4422  The lesson from Google One Pass: In this digit...             9   \n",
       "4     5526  RT @mention At the panel: &quot;Your mom has a...             9   \n",
       "\n",
       "   Sentiment  \n",
       "0          2  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Text_ID', axis=1, inplace=True)\n",
    "test.drop('Text_ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.rename(columns={'Product_Description' : 'text', 'Sentiment': 'labels'})\n",
    "test = test.rename(columns={'Product_Description' : 'text'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "target = 'labels'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############  D A T A   S E T  A N A L Y S I S  #######################\n",
      "Training Set Shape = (6364, 3)\n",
      "    Training Set Memory Usage = 0.15 MB\n",
      "Test Set Shape = (2728, 2)\n",
      "    Test Set Memory Usage = 0.04 MB\n",
      "Single_Label Target: ['labels']\n",
      "ALERT! Setting Imbalanced_Flag to True in Auto_ViML for Multi_Classification problems improves results!\n",
      "Shuffling the data set before training\n",
      "       Class  -> Counts -> Percent\n",
      "           0:     111  ->    1.7%\n",
      "           1:     399  ->    6.3%\n",
      "           2:    3765  ->   59.2%\n",
      "           3:    2089  ->   32.8%\n",
      "Using RandomizedSearchCV for Hyper Parameter Tuning. This is 3X faster than GridSearchCV...\n",
      "    Target labels is already numeric. No transformation done.\n",
      "Alert! Rare Class is not 1 but 0 in this data set\n",
      "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
      "Classifying variables in data set...\n",
      "    2 Predictors classified...\n",
      "        This does not include the Target column(s)\n",
      "    No variables removed since no ID or low-information variables found in data set\n",
      "Number of GPUs = 2\n",
      "No GPU available on this device\n",
      "#############     D A T A    P R E P A R A T I O N   AND C L E A N I N G     #############\n",
      "No Missing Values in train data set\n",
      "Test data has no missing values. Continuing...\n",
      "Completed Label Encoding and Filling of Missing Values for Train and Test Data\n",
      "Multi_Classification problem: hyperparameters are being optimized for balanced_accuracy\n",
      "############## F E A T U R E   S E L E C T I O N  ####################\n",
      "Removing highly correlated features among 1 variables using pearson correlation...\n",
      "    No variables were removed since no highly correlated variables found in data\n",
      "\n",
      "############# PROCESSING T A R G E T = labels ##########################\n",
      "Auto NLP processing on NLP Column: text\n",
      "Shape of Train Data: 6364 rows\n",
      "    Shape of Test Data: 2728 rows\n",
      "    Added 9 summary columns for counts of words and characters in each row\n",
      "    Cleaning text in text before doing transformation...\n",
      "Cleaning text in Train data\n",
      "    Faster text processing using clean_tweets function, since number of rows exceeds 10000\n",
      "Train data Text cleaning completed. Time taken = 9 seconds\n",
      "\n",
      "For class = 0\n",
      "Top 200 n-grams\n",
      ": ['mention', 'link', 'googl', 'ipad', 'iphon', 'appl', 'retweet', 'circl', 'store', 'austin', 'not', 'go', 'will', 'product', 'do', 'line', 'but', 'like', 'call', 'popup', 'your', 'applic', 'think', 'launch', 'no', 'android', 'game', 'today', 'map', 'yet', 'batteri', 'need', 'might', 'their', 'day', 'what', 'we', 'take', 'would', 'network', 'social', 'they', 'gave', 'give', 'fast', 'ha', 'thank', 'buy', 'new', 'sxswi', 'compani', 'parti', 'also', 'en', 'good', 'panel', 'cc', 'juic', 'daili', 'news', 'long', 'how', 'should', 'there', 'diller', 'use', 'as', 'present', 'set', 'seen', 'mac', 'let', 'panelist', 'quotth', 'tweet', 'rest', 'miss', 'newsapp', 'laptop', 'easi', 'bet', 'sxswquot', 'time', 'know', 'vs', 'shop', 'reaction', 'possibl', 'old', 'brand', 'sold', 'been', 'form', 'funni', 'mine', 'well', 'befor', 'play', 'design', 'ave', 'stock', 'barri', 'when', 'search', 'me', 'check', 'uxd', 'probabl', 'four', 'tv', 'place', 'wow', 'after', 'turn', 'better', 'look', 'win', 'enough', 'sell', 'temporari', 'doe', 'cannot', 'whi', 'hope', 'room', 'open', 'gaga', 'intel', 'our', 'off', 'two', 'fail', 'amp', 'retweet mention', 'sxsw link', 'appl store', 'mention sxsw', 'link sxsw', 'googl circl', 'mention mention', 'popup store', 'austin sxsw', 'appl sxsw', 'iphon batteri', 'store austin', 'googl map', 'ipad sxsw', 'link mention', 'mention googl', 'social network', 'applic sxsw', 'sxsw mention', 'thank mention', 'doe not', 'ipad should', 'sxsw will', 'your iphon', 'sxsw appl', 'circl will', 'day sxsw', 'cc mention', 'buy ipad', 'android parti', 'team android', 'barri diller', 'mention appl', 'retweet mention mention', 'appl store austin', 'appl popup store', 'sxsw appl store', 'team android parti', 'sxsw link mention', 'sxsw sell ipad', 'googl circl will', 'austin sxsw link', 'network call circl', 'social network call', 'link cc mention', 'circl will sxsw', 'launch social network', 'mention googl circl', 'retweet mention googl', 'call circl possibl', 'mention sxsw mention', 'retweet mention sxsw', 'appl googl intel', 'gaga go game', 'go gaga go', 'googl intel go', 'intel go gaga', 'anoth holi social', 'circl possibl day', 'day sxsw link', 'fragmentationgtgoogl launch new', 'holi social fragmentationgtgoogl', 'launch new social', 'new social network', 'possibl day sxsw', 'social fragmentationgtgoogl launch']\n",
      "    Top n-grams that are most frequent in this class are: 199\n",
      "\n",
      "For class = 1\n",
      "Top 200 n-grams\n",
      ": ['mention', 'ipad', 'iphon', 'not', 'googl', 'retweet', 'link', 'appl', 'applic', 'store', 'like', 'will', 'do', 'design', 'new', 'social', 'but', 'circl', 'launch', 'need', 'peopl', 'use', 'ha', 'no', 'if', 'headach', 'whi', 'think', 'me', 'as', 'go', 'austin', 'your', 'line', 'what', 'they', 'call', 'there', 'android', 'becaus', 'today', 'come', 'take', 'time', 'doe', 'popup', 'fail', 'now', 'would', 'look', 'day', 'how', 'news', 'phone', 'compani', 'when', 'wait', 'realli', 'major', 'fascist', 'tapworthi', 'here', 'walk', 'did', 'user', 'network', 'suck', 'year', 'tweet', 'long', 'tri', 'anoth', 'much', 'their', 'money', 'better', 'talk', 'should', 'want', 'alreadi', 'yet', 'quoti', 'amp', 'see', 'fast', 'first', 'mani', 'best', 'know', 'back', 'map', 'thing', 'good', 'japan', 'content', 'instead', 'product', 'panel', 'noth', 'session', 'also', 'everyon', 'autocorrect', 'batteri', 'work', 'crash', 'guy', 'g', 'diller', 'w', 'quot', 'we', 'made', 'watch', 'sxswi', 'guess', 'gave', 'thought', 'possibl', 'heard', 'blackberri', 'among', 'deleg', 'fade', 'novelti', 'ever', 'calendar', 'parti', 'way', 'hand', 'classiest', 'twitter', 'updat', 'retweet mention', 'mention sxsw', 'appl store', 'ipad sxsw', 'sxsw link', 'iphon sxsw', 'mention googl', 'iphon applic', 'link sxsw', 'design headach', 'sxsw appl', 'ipad design', 'do not', 'doe not', 'will not', 'googl circl', 'googl map', 'mention mention', 'new social', 'did not', 'ipad applic', 'fascist compani', 'news applic', 'sxsw applic', 'social network', 'new ipad', 'major new', 'marissa mayer', 'sxsw iphon', 'googl launch', 'call circl', 'network call', 'today link', 'retweet mention googl', 'ipad design headach', 'sxsw iphon applic', 'major new social', 'retweet mention mention', 'popup appl store', 'launch major new', 'sxsw appl store', 'retweet mention sxsw', 'classiest fascist compani', 'network call circl', 'new social network', 'social network call', 'ipad news applic', 'do not need', 'mention mention mention', 'kara swisher sxsw', 'design headach sxsw', 'fascist compani americaquot', 'googl launch major', 'applic fade fast', 'fade fast among', 'news applic fade', 'novelti ipad news', 'today link sxsw', 'appl pop store', 'appl store mall', 'build netflix iphon', 'diller googl tv', 'if did not', 'ipad will not', 'iphon stop work', 'iphon user sxsw']\n",
      "    Top n-grams that are most frequent in this class are: 199\n",
      "\n",
      "For class = 2\n",
      "Top 200 n-grams\n",
      ": ['googl', 'retweet', 'ipad', 'appl', 'store', 'launch', 'new', 'iphon', 'austin', 'social', 'circl', 'today', 'network', 'not', 'popup', 'amp', 'applic', 'open', 'call', 'android', 'line', 'will', 'major', 'possibl', 'your', 'parti', 'free', 'do', 'temporari', 'if', 'go', 'sxswi', 'mobil', 'what', 'now', 'we', 'me', 'check', 'ha', 'here', 'downtown', 'come', 'win', 'pop', 'set', 'map', 'rumor', 'mayer', 'but', 'day', 'like', 'need', 'time', 'there', 'use', 'see', 'shop', 'our', 'make', 'peopl', 'know', 'how', 'no', 'design', 'talk', 'facebook', 'marissa', 'look', 'w', 'th', 'download', 'product', 'want', 'they', 'guy', 'music', 'show', 'anyon', 'tech', 'search', 'news', 'congress', 'thank', 'panel', 'take', 'who', 'dure', 'think', 'first', 'game', 'updat', 'their', 'as', 'locat', 'event', 'head', 'develop', 'includ', 'video', 'code', 'itun', 'last', 'digit', 'great', 'session', 'find', 'tweet', 'pm', 'big', 'year', 'bing', 'around', 'futur', 'did', 'blackberri', 'photo', 'where', 'give', 'best', 'right', 'user', 'next', 'way', 'connect', 'doe', 'twitter', 'wait', 'follow', 'start', 'block', 'wonder', 'checkin', 'would', 'retweet mention', 'sxsw link', 'link sxsw', 'social network', 'appl store', 'mention sxsw', 'mention mention', 'new social', 'mention googl', 'googl launch', 'network call', 'call circl', 'major new', 'launch major', 'store sxsw', 'link mention', 'appl open', 'ipad sxsw', 'possibl today', 'circl possibl', 'austin sxsw', 'popup store', 'today link', 'sxsw mention', 'sxsw ipad', 'mention appl', 'sxsw appl', 'googl circl', 'mention retweet', 'store austin', 'mention link', 'appl popup', 'sxsw sxswi', 'new social network', 'social network call', 'network call circl', 'major new social', 'launch major new', 'retweet mention googl', 'googl launch major', 'circl possibl today', 'call circl possibl', 'mention googl launch', 'possibl today link', 'today link sxsw', 'mention retweet mention', 'retweet mention sxsw', 'appl popup store', 'appl store sxsw', 'mention mention mention', 'retweet mention retweet', 'retweet mention mention', 'retweet mention appl', 'appl open temporari', 'store sxsw link', 'popup appl store', 'open temporari store', 'store downtown austin', 'sxsw appl store', 'sxsw link mention', 'appl open popup', 'popup store austin', 'austin sxsw ipad', 'ipad launch link', 'launch new social', 'popup store sxsw']\n",
      "    Top n-grams that are most frequent in this class are: 199\n",
      "\n",
      "For class = 3\n",
      "Top 200 n-grams\n",
      ": ['link', 'ipad', 'appl', 'retweet', 'googl', 'store', 'iphon', 'applic', 'new', 'austin', 'popup', 'not', 'launch', 'open', 'android', 'win', 'amp', 'go', 'will', 'line', 'parti', 'come', 'time', 'now', 'social', 'your', 'use', 'cool', 'great', 'circl', 'map', 'today', 'they', 'do', 'day', 'awesom', 'like', 'downtown', 'ha', 'free', 'check', 'love', 'even', 'temporari', 'but', 'network', 'befor', 'me', 'we', 'thank', 'mobil', 'sxswi', 'good', 'no', 'here', 'look', 'see', 'what', 'begin', 'there', 'call', 'w', 'if', 'our', 'take', 'market', 'want', 'how', 'peopl', 'set', 'major', 'pop', 'need', 'video', 'year', 'user', 'first', 'make', 'nice', 'next', 'tech', 'fun', 'possibl', 'design', 'wait', 'show', 'shop', 'mayer', 'their', 'smart', 'case', 'ever', 'who', 'know', 'as', 'save', 'give', 'best', 'around', 'heard', 'work', 'buy', 'marissa', 'game', 'week', 'updat', 'technolog', 'wow', 'would', 'ye', 'sell', 'becaus', 'rumor', 'temp', 'realli', 'big', 'panel', 'dure', 'phone', 'live', 'talk', 'photo', 'excit', 'had', 'includ', 'thing', 'quotappl', 'featur', 'think', 'team', 'right', 'rock', 'music', 'retweet mention', 'sxsw link', 'appl store', 'mention sxsw', 'link sxsw', 'ipad sxsw', 'iphon applic', 'mention mention', 'store sxsw', 'popup store', 'sxsw ipad', 'link mention', 'sxsw appl', 'appl open', 'mention googl', 'austin sxsw', 'ipad applic', 'googl map', 'sxsw mention', 'social network', 'mention appl', 'mention ipad', 'new ipad', 'new social', 'appl popup', 'do not', 'downtown austin', 'googl launch', 'temporari store', 'store downtown', 'ipad launch', 'open popup', 'googl parti', 'new social network', 'retweet mention googl', 'retweet mention sxsw', 'appl popup store', 'retweet mention mention', 'appl store sxsw', 'sxsw appl store', 'store downtown austin', 'even begin appl', 'social network call', 'befor even begin', 'launch major new', 'major new social', 'network call circl', 'appl win sxsw', 'open temporari store', 'mention retweet mention', 'retweet mention appl', 'retweet mention ipad', 'googl launch major', 'begin appl win', 'popup store sxsw', 'open popup store', 'win sxsw link', 'popup appl store', 'downtown austin sxsw', 'appl open popup', 'call circl possibl', 'circl possibl today', 'store sxsw link', 'appl open temporari', 'retweet mention retweet', 'googl marissa mayer']\n",
      "    Top n-grams that are most frequent in this class are: 199\n",
      "Time Taken = 1 seconds\n",
      "##################    THIS IS FOR BUILD_MODEL = FALSE           #################\n",
      "Building Model and Pipeline for NLP column = text. This will take time...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Selected the maximum number of features limit = 19175\n",
      "Performing RandomizedSearchCV across 30 params. Optimizing for accuracy\n",
      "    Using train data = (5409,) and Cross Validation data = (955,)\n",
      "Using a Calibrated Classifier in this Multi_Classification dataset to improve results...\n",
      "Since top_num_features < 300, Multinomial NB model selected. If you need different model, increase it >= 300.\n",
      "Training completed. Time taken for training = 0.4 minutes\n",
      "Best Params of NLP pipeline are: {'multinomialnb__alpha': 0.41166323884293843, 'selectkbest__k': 10792, 'tfidfvectorizer__binary': True, 'tfidfvectorizer__encoding': 'utf-8', 'tfidfvectorizer__max_df': 0.08039991715696582}\n"
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Now transforming Train data to return as output...\n",
      "  Transforming Test data to return as output...\n",
      "##################    AFTER BEST NLP TRANSFORMER SELECTED, NOW ENRICH TEXT DATA  #####################\n",
      "    Now we will start transforming NLP_column for train and test data using best vectorizer...\n",
      "Reducing dimensions from 10792 term-matrix to 100 dimensions using TruncatedSVD...\n",
      "    Reduced dimensional array shape to (6364, 100)\n",
      "    Time Taken for Truncated SVD = 8 seconds\n",
      "TruncatedSVD Data Frame size = (6364, 100)\n",
      "Reducing dimensions from 10792 term-matrix to 100 dimensions using TruncatedSVD...\n",
      "    Reduced dimensional array shape to (2728, 100)\n",
      "    Time Taken for Truncated SVD = 0 seconds\n",
      "TruncatedSVD Data Frame size = (2728, 100)\n",
      "Creating word clusters using term matrix of size: 6364 for Train data set...\n",
      "    Running k-means on NLP token matrix to create 5 word clusters.\n",
      "    Created one new column: text_word_cluster_label using KMeans_Clusters on NLP transformed columns...\n",
      "    Running k-means on NLP token matrix to create 5 word clusters.\n",
      "    Created word clusters using NLP transformed matrix for Test data set...\n",
      "    Time Taken for creating word cluster labels  = 10 seconds\n",
      "Using Vader to calculate objectivity and pos-neg-neutral scores\n",
      "    Created 4 new columns using SentinmentIntensityAnalyzer. Time taken = 3 seconds\n",
      "Number of new columns created using NLP = 116\n",
      "Time taken for Auto_NLP to complete = 1.0 minutes\n",
      "#########          A U T O   N L P  C O M P L E T E D    ###############################\n",
      "No categorical feature reduction done. All 12 Categorical vars selected \n",
      "############## F E A T U R E   S E L E C T I O N  ####################\n",
      "Removing highly correlated features among 104 variables using pearson correlation...\n",
      "    Number of variables removed due to high correlation = 2 \n",
      "    Adding 12 categorical variables to reduced numeric variables  of 102\n",
      "############## F E A T U R E   S E L E C T I O N  ####################\n",
      "Current number of predictors = 114 \n",
      "    Finding Important Features using Boosted Trees algorithm...\n",
      "        using 114 variables...\n",
      "        using 91 variables...\n",
      "        using 68 variables...\n",
      "        using 45 variables...\n",
      "        using 22 variables...\n",
      "Found 68 important features\n",
      "Starting Feature Engineering now...\n",
      "Train CV Split completed with TRAIN rows: 5454 CV rows: 910\n",
      "    Binning_Flag set to False or there are no numeric vars in data set to be binned\n",
      "    KMeans_Featurizer set to False or there are no numeric vars in data\n",
      "Feature scaling for total 68 float and integer variables completed using MinMaxScaler()...\n",
      "###############  M O D E L   B U I L D I N G  B E G I N S  ####################\n",
      "Rows in Train data set = 5454\n",
      "  Features in Train data set = 68\n",
      "    Rows in held-out data set = 910\n",
      "Finding Best Model and Hyper Parameters for Target: labels...\n",
      "    Baseline Accuracy Needed for Model = 98.26%\n",
      "CPU Count = 4 in this device\n",
      "Using Forests Model, Estimated Training time = 14.83 mins\n",
      "    Actual training time (in seconds): 286\n",
      "###########  S I N G L E  M O D E L   R E S U L T S #################\n",
      "7-fold Cross Validation logloss = 0.843879976531665\n",
      "    Best Parameters for Model = {'max_depth': 6, 'min_samples_leaf': 2, 'n_estimators': 231}\n",
      "Using a Calibrated Classifier in this Multi_Classification dataset to improve results...\n",
      "Best Model selected and its parameters are:\n",
      "    CalibratedClassifierCV(base_estimator=RandomForestClassifier(max_depth=6,\n",
      "                                                             min_samples_leaf=2,\n",
      "                                                             n_estimators=231,\n",
      "                                                             n_jobs=-1,\n",
      "                                                             oob_score=True,\n",
      "                                                             random_state=99,\n",
      "                                                             warm_start=True),\n",
      "                       cv='prefit')\n",
      "########################################################\n",
      "Forests Model Prediction Results on Held Out CV Data Set:\n",
      "    OOB Score = 0.877\n",
      "    Balanced Accuracy Score = 51.9%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        17\n",
      "           1       0.61      0.22      0.32        50\n",
      "           2       0.91      0.97      0.94       538\n",
      "           3       0.85      0.88      0.87       305\n",
      "\n",
      "    accuracy                           0.88       910\n",
      "   macro avg       0.59      0.52      0.53       910\n",
      "weighted avg       0.86      0.88      0.86       910\n",
      "\n",
      "[[  0   0  15   2]\n",
      " [  0  11   5  34]\n",
      " [  2   2 523  11]\n",
      " [  0   5  31 269]]\n",
      "################# E N S E M B L E  M O D E L  ##################\n",
      "Time taken = 3 seconds\n",
      "Based on trying multiple models, Best type of algorithm for this data set is Linear_Discriminant\n",
      "    Displaying results of weighted average ensemble of 5 classifiers\n",
      "#############################################################################\n",
      "After multiple models, Ensemble Model Results:\n",
      "    Balanced Accuracy Score = 51.852%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        17\n",
      "           1       0.61      0.22      0.32        50\n",
      "           2       0.91      0.97      0.94       538\n",
      "           3       0.85      0.88      0.87       305\n",
      "\n",
      "    accuracy                           0.88       910\n",
      "   macro avg       0.59      0.52      0.53       910\n",
      "weighted avg       0.86      0.88      0.86       910\n",
      "\n",
      "[[  0   0  15   2]\n",
      " [  0  11   5  34]\n",
      " [  2   2 523  11]\n",
      " [  0   5  31 269]]\n",
      "#############################################################################\n",
      "Single Model is better than Ensembling Models for this data set.\n",
      "    Time taken for this Target (in seconds) = 399\n",
      "Training model on complete Train data and Predicting using give Test Data...\n",
      "    Binning_Flag set to False or there are no numeric vars in data set to be binned\n",
      "Feature scaling for total 68 float and integer variables completed using MinMaxScaler()...\n",
      "Actual Training time taken in seconds = 0\n",
      "    Calculating weighted average ensemble of 5 classifiers\n",
      "Plotting Feature Importances to explain the output of model\n",
      "############### P R E D I C T I O N  O N  T E S T   C O M P L E T E D  #################\n",
      "    Time taken for this Target (in seconds) = 403\n",
      "Writing Output files to disk...\n",
      "    Saving predictions to ./labels/labels_Multi_Classification_test_modified.csv\n",
      "    Saving predictions to ./labels/labels_Multi_Classification_submission.csv\n",
      "    Saving predictions to ./labels/labels_Multi_Classification_train_modified.csv\n",
      "###############  C O M P L E T E D  ################\n",
      "Time Taken in mins = 6.7 for the Entire Process\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, features, trainm, testm = Auto_ViML(\n",
    "    train,\n",
    "    target,\n",
    "    test,\n",
    "    samp,\n",
    "    hyper_param=\"RS\",\n",
    "    feature_reduction=True,\n",
    "    scoring_parameter=\"neg_log_loss\",\n",
    "    KMeans_Featurizer=False,\n",
    "    Boosting_Flag=False,\n",
    "    Binning_Flag=False,\n",
    "    Add_Poly=False,\n",
    "    Stacking_Flag=False,\n",
    "    Imbalanced_Flag=False,\n",
    "    verbose=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>text_vader_neg</th>\n",
       "      <th>text_vader_pos</th>\n",
       "      <th>svd_dim_22</th>\n",
       "      <th>svd_dim_48</th>\n",
       "      <th>svd_dim_24</th>\n",
       "      <th>svd_dim_11</th>\n",
       "      <th>svd_dim_71</th>\n",
       "      <th>svd_dim_9</th>\n",
       "      <th>svd_dim_68</th>\n",
       "      <th>...</th>\n",
       "      <th>labels_Linear_Discriminant_predictions</th>\n",
       "      <th>labels_Linear_SVC_predictions</th>\n",
       "      <th>labels_Naive_Bayes_predictions</th>\n",
       "      <th>labels_One_vs_Rest_Classifier_predictions</th>\n",
       "      <th>labels_Forests_predictions</th>\n",
       "      <th>labels_proba_0</th>\n",
       "      <th>labels_proba_1</th>\n",
       "      <th>labels_proba_2</th>\n",
       "      <th>labels_proba_3</th>\n",
       "      <th>labels_Ensembled_predictions</th>\n",
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       "      <td>0.78</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Type  text_vader_neg  text_vader_pos  svd_dim_22  svd_dim_48  \\\n",
       "0          0.78            0.10            0.36        0.28        0.51   \n",
       "\n",
       "   svd_dim_24  svd_dim_11  svd_dim_71  svd_dim_9  svd_dim_68  ...  \\\n",
       "0        0.41        0.45        0.55       0.48        0.45  ...   \n",
       "\n",
       "   labels_Linear_Discriminant_predictions  labels_Linear_SVC_predictions  \\\n",
       "0                                       3                              3   \n",
       "\n",
       "   labels_Naive_Bayes_predictions  labels_One_vs_Rest_Classifier_predictions  \\\n",
       "0                               2                                          3   \n",
       "\n",
       "   labels_Forests_predictions  labels_proba_0  labels_proba_1  labels_proba_2  \\\n",
       "0                           3            0.00            0.02            0.02   \n",
       "\n",
       "   labels_proba_3  labels_Ensembled_predictions  \n",
       "0            0.96                             3  \n",
       "\n",
       "[1 rows x 79 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testm.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp[['0','1','2','3']] = testm[['labels_proba_0','labels_proba_1','labels_proba_2','labels_proba_3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp = samp.drop(['labels'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 23,
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   "source": [
    "samp.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2728, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "samp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Train.csv\")\n",
    "test = pd.read_csv(\"/kaggle/input/machine-hack-product-sentiment-classification/Participants_Data/Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Text_ID', 'Product_Description', 'Product_Type', 'Sentiment'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ids = []\n",
    "force_senti = []\n",
    "for index,row in test.iterrows():\n",
    "    temp = train[train['Product_Description'] == row['Product_Description']]\n",
    "    temp = temp[temp['Product_Type'] == row['Product_Type']]\n",
    "    if temp.shape[0] > 0:\n",
    "        force_senti.append(list(set(temp['Sentiment'].tolist()))[0])\n",
    "        test_ids.append(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {0:[1,0,0,0],1:[0,1,0,0],2:[0,0,1,0],3:[0,0,0,1]}\n",
    "for x,y in zip(test_ids,force_senti):\n",
    "    #print(s1.iloc[x])\n",
    "    target = dic[y]\n",
    "    samp.iloc[x] = target\n",
    "    #print(s1.iloc[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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    "samp.head(3)"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp.to_csv('Sub_v0.4.csv', index=False)"
   ]
  }
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